ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed

Cheonbok Park · Chunggi Lee · Hyojin Bahng · Yunwon Tae · Seungmin Jin · Ki-Hwan Kim +2 more

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Traffic Prediction and Management Techniques Traffic control and management Air Quality Monitoring and Forecasting

Abstract

Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences. This paper proposes a novel spatio-temporal graph attention (ST-GRAT) that effectively captures the spatio-temporal dynamics in road networks. The novel aspects of our approach mainly include spatial attention, temporal attention, and spatial sentinel vectors. The spatial attention takes the graph structure information (e.g., distance between roads) and dynamically adjusts spatial correlation based on road states. The temporal attention is responsible for capturing traffic speed changes, and the sentinel vectors allow the model to retrieve new features from spatially correlated nodes or preserve existing features. The experimental results show that ST-GRAT outperforms existing models, especially in difficult conditions where traffic speeds rapidly change (e.g., rush hours). We additionally provide a qualitative study to analyze when and where ST-GRAT tended to make accurate predictions during rush-hour times.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2019Graph WaveNet for Deep Spatial-Temporal Graph Modeling 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2020GMAN: A Graph Multi-Attention Network for Traffic Prediction 2017LSTM network: a deep learning approach for short‐term traffic forecast 2014Short-term traffic forecasting: Where we are and where we’re going 2014Big data and its technical challenges 2018Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

Cited by

2022Graph neural network for traffic forecasting: A survey 2021Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting 2022Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions 2022Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution 2022Decoupled dynamic spatial-temporal graph neural network for traffic forecasting

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